SNSF Leading House Asia Swiss-Chinese Collaboration 2024-2025

Graph-Theoretic Analysis for Consumer Credit Risk Assessment

Applying advanced graph neural networks to enhance credit risk prediction in China's consumer lending market

4Work Packages
7Researchers
8+Publications

Research Team

Prof. Joerg Osterrieder

Prof. Joerg Osterrieder

Principal Investigator (CH)

University of Twente
Prof. Jeffrey Chu

Prof. Jeffrey Chu

Principal Investigator (CN)

Renmin University
Prof. Stephen Chan

Prof. Stephen Chan

Co-Investigator

American University
Prof. Yuanyuan Zhang

Prof. Yuanyuan Zhang

Co-Investigator

AUS / Manchester
Yiting Liu

Yiting Liu

PhD Researcher

UT & BFH
Lennart Baals

Lennart Baals

PhD Researcher

UT & BFH
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Graph Neural Networks

Advanced GNN models for credit risk prediction

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Swiss-Asian Collaboration

BFH Switzerland & Renmin University China

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Consumer Lending

Focus on P2P and BNPL markets in China

Latest News

CIEP 2025 Shanghai
CONFERENCE

CIEP 2025 Shanghai - Presentation

Prof. Osterrieder presented on Digital Finance, LLM Narratives, and High-Frequency Trading at the 23rd China International Talent Exchange Conference.

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Yiting Liu at Chinese Embassy
AWARD

Yiting Liu Receives 2024 Chinese Government Award

PhD researcher Yiting Liu received the prestigious Chinese Government Award for Outstanding Self-Financed Students Abroad (Category A).

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International Week Shenzhen
PRESS RELEASE

Project Concludes with 8+ Publications

Swiss-Chinese collaboration completed its 21-month program with innovations in graph-based credit risk assessment.

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Project Timeline

Recent Publications

Credit Risk Prediction via Graph Neural Networks with Homophily-Guided Graph Construction

Journal of Management Information Systems (JMIS) - Under Review

GNN Credit Risk

Why are global P2P lending platforms exiting peer-to-peer models?

Comparative analysis of risks and regulations

P2P Lending

Multilayer Topology-Aware Graph Contrastive Learning for Fraud Detection

Ethereum Transaction Network Analysis

Fraud Detection

Resources